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|Title:||A clustering based approach for energy efficient routing||Authors:||Kosmides, Pavlos
|Keywords:||Clustering based prediction;Energy efficiency;Intelligent Transport Systems;Machine learning||Category:||Civil Engineering;Civil Engineering||Field:||Engineering and Technology||Issue Date:||15-Aug-2016||Publisher:||Institute of Electrical and Electronics Engineers Inc.||Source:||IEEE Symposium on Computers and Communications, 2016, Messina, Italy||metadata.dc.doi:||10.1109/ISCC.2016.7543745||Abstract:||One of the most significant issues the research community has focused on during the last decades, is the reduction of the energy consumed in every aspect of everyday life. A standout amongst the most important factors of energy consumption is transportation. To this end, a lot of work in the field of Intelligent Transport Systems concentrates on enhancing energy efficiency. This trend was reinforced by the appearance of Fully Electric Vehicles (FEVs), where it is more crucial to increase their energy efficiency in any manner. Eco-routing refers to the choice of the most energy efficient route towards a destination and seems very promising for reducing everyday energy consumption. In this paper, we present a novel method for predicting energy consumption levels, based on machine learning techniques. In addition, addressing the problem of ever increasing amounts of tracking data acquired from vehicles, we introduce a clustering based prediction method and apply it on real world measurements in order to evaluate its performance.||URI:||http://ktisis.cut.ac.cy/handle/10488/9025||ISBN:||978-150900679-3||Rights:||© 2016 IEEE||Type:||Conference Papers|
|Appears in Collections:||Δημοσιεύσεις σε συνέδρια/Conference papers|
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